diff --git a/openbb_terminal/jupyter/dashboards/chains.ipynb b/openbb_terminal/jupyter/dashboards/chains.ipynb new file mode 100644 index 00000000000..c17412479ee --- /dev/null +++ b/openbb_terminal/jupyter/dashboards/chains.ipynb @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "e9ad60bc", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import ipywidgets as widgets\n", + "import plotly.graph_objs as go\n", + "import yfinance as yf\n", + "from IPython.display import display" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "beb36881", + "metadata": {}, + "outputs": [], + "source": [ + "opts = [\n", + " \"lastTradeDate\",\n", + " \"strike\",\n", + " \"lastPrice\",\n", + " \"bid\",\n", + " \"ask\",\n", + " \"change\",\n", + " \"percentChange\",\n", + " \"volume\",\n", + " \"openInterest\",\n", + " \"impliedVolatility\",\n", + "]\n", + "\n", + "\n", + "def clean_str(string):\n", + " new_str = \"\"\n", + " for letter in string:\n", + " if letter.isupper():\n", + " new_str += \" \"\n", + " new_str += letter\n", + " return new_str.title()\n", + "\n", + "\n", + "def format_plotly(fig, x, y, ticker, expiry, inst):\n", + " fig.update_yaxes(title=clean_str(y))\n", + " fig.update_xaxes(title=clean_str(x))\n", + " expires = \", \".join(expiry)\n", + " title = (\n", + " f\"{clean_str(y)} vs. {clean_str(x)} for {ticker.upper()} {inst}s on {expires}\"\n", + " )\n", + " fig.update_layout(\n", + " autosize=False,\n", + " width=1000,\n", + " height=500,\n", + " title={\n", + " \"text\": title,\n", + " \"y\": 0.95,\n", + " \"x\": 0.5,\n", + " \"xanchor\": \"center\",\n", + " \"yanchor\": \"top\",\n", + " },\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8c2f65c", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "class Chart:\n", + " def __init__(self):\n", + " self.last_ticker = \"\"\n", + " self.dfs = {}\n", + "\n", + " def update(self, ticker, expiry, inst, x, y):\n", + " if ticker:\n", + " stock = yf.Ticker(ticker)\n", + " if ticker != self.last_ticker:\n", + " exp_w.options = stock.options\n", + " self.last_ticker = ticker\n", + "\n", + " if expiry:\n", + " fig = go.Figure()\n", + " for expire in expiry:\n", + " if expire not in self.dfs:\n", + " self.dfs[expire] = stock.option_chain(expire)\n", + " group = self.dfs[expire]\n", + " df = group.calls if inst == \"Call\" else group.puts\n", + "\n", + " plot = go.Scatter(\n", + " x=df[x],\n", + " y=df[y],\n", + " mode=\"lines\",\n", + " connectgaps=True,\n", + " name=expire,\n", + " )\n", + " fig.add_trace(plot)\n", + "\n", + " format_plotly(fig, x, y, ticker, expiry, inst)\n", + "\n", + " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", + " fig.show(config={\"showTips\": False}, renderer=\"notebook\")\n", + " else:\n", + " fig.show(config={\"showTips\": False})\n", + "\n", + "\n", + "x_w = widgets.Dropdown(options=opts, value=\"strike\", description=\"X\")\n", + "y_w = widgets.Dropdown(options=opts, value=\"lastPrice\", description=\"Y\")\n", + "tickers_w = widgets.Text(description=\"Ticker\")\n", + "exp_w = widgets.SelectMultiple(description=\"Expiry\")\n", + "inst_w = widgets.Dropdown(options=[\"Put\", \"Call\"], value=\"Call\", description=\"Type\")\n", + "\n", + "select_opt = widgets.VBox([tickers_w, exp_w])\n", + "select_plot = widgets.VBox([inst_w, x_w, y_w])\n", + "controls = widgets.HBox([select_opt, select_plot])\n", + "\n", + "chart = Chart()\n", + "stocks_view = widgets.interactive_output(\n", + " chart.update,\n", + " {\"ticker\": tickers_w, \"expiry\": exp_w, \"inst\": inst_w, \"x\": x_w, \"y\": y_w},\n", + ")\n", + "\n", + "title_html = \"

Option Chain Dashboard

\"\n", + "app_contents = [\n", + " widgets.HTML(title_html),\n", + " controls,\n", + " stocks_view,\n", + "]\n", + "app = widgets.VBox(app_contents)\n", + "display(app)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a84c7390-6b43-4a25-8aa2-31aa84f52605", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "metadata": { + "interpreter": { + "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/jupyter/dashboards/correlation.ipynb b/openbb_terminal/jupyter/dashboards/correlation.ipynb new file mode 100644 index 00000000000..7e2b67e59a0 --- /dev/null +++ b/openbb_terminal/jupyter/dashboards/correlation.ipynb @@ -0,0 +1,222 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "id": "e9ad60bc", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from datetime import datetime, timedelta\n", + "import ipywidgets as widgets\n", + "import plotly.graph_objs as go\n", + "import yfinance as yf\n", + "import pandas as pd\n", + "from IPython.display import display" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6c554cd2-462c-4832-b73c-c214aae11ce4", + "metadata": {}, + "outputs": [], + "source": [ + "interval_opts = [\n", + " \"1m\",\n", + " \"2m\",\n", + " \"5m\",\n", + " \"15m\",\n", + " \"30m\",\n", + " \"60m\",\n", + " \"90m\",\n", + " \"1h\",\n", + " \"1d\",\n", + " \"5d\",\n", + " \"1wk\",\n", + " \"1mo\",\n", + " \"3mo\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f8c2f65c", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b462032b043b47a8899f8366f43ab28e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='

Correlation Analysis Dashboard

'), HBox(children=(VBox(children=(Textarea(v…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "class Chart:\n", + " def __init__(self):\n", + " self.last_tickers = \"\"\n", + " self.last_interval = \"1d\"\n", + " self.df = pd.DataFrame()\n", + "\n", + " def create(self, data, start, end, interval, tickers):\n", + " global last_tickers, df, last_interval\n", + " if tickers and tickers[-1] == \",\":\n", + " if tickers != self.last_tickers or interval != self.last_interval:\n", + " if interval in [\"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"]:\n", + " self.df = yf.download(\n", + " tickers, period=\"max\", interval=interval, progress=False\n", + " )\n", + " else:\n", + " self.df = yf.download(\n", + " tickers, start=start, end=end, interval=interval, progress=False\n", + " )\n", + " self.last_tickers = tickers\n", + " self.last_interval = interval\n", + "\n", + " start_n = datetime(start.year, start.month, start.day)\n", + " end_n = datetime(end.year, end.month, end.day)\n", + "\n", + " df = self.df[data]\n", + "\n", + " if not isinstance(df, pd.Series):\n", + " if interval in [\"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"]:\n", + " result = df.loc[(df.index >= start_n) & (df.index <= end_n)].corr()\n", + " else:\n", + " result = df.corr()\n", + "\n", + " base = [\n", + " [\n", + " \"black\" if x == 1 else \"lightgreen\" if x > 0 else \"lightpink\"\n", + " for x in result[y].tolist()\n", + " ]\n", + " for y in result.columns\n", + " ]\n", + " base = [[\"lightgray\" for _ in range(result.shape[0])]] + base\n", + " result = result.reset_index()\n", + " result.rename(columns={\"index\": \"\"}, inplace=True)\n", + "\n", + " fig = go.Figure(\n", + " data=[\n", + " go.Table(\n", + " header=dict(\n", + " values=list(result.columns),\n", + " fill_color=\"lightgray\",\n", + " font=dict(color=\"black\"),\n", + " align=\"left\",\n", + " ),\n", + " cells=dict(\n", + " values=[result[x] for x in result.columns],\n", + " fill_color=base,\n", + " format=[\"\"]\n", + " + [\".2f\" for _ in range(len(df.columns) - 1)],\n", + " font=dict(color=\"black\"),\n", + " align=\"left\",\n", + " ),\n", + " )\n", + " ],\n", + " )\n", + " fig.update_layout(\n", + " autosize=True,\n", + " height=600,\n", + " showlegend=False,\n", + " )\n", + " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", + " fig.show(config={\"showTips\": False}, renderer=\"notebook\")\n", + " else:\n", + " fig.show(config={\"showTips\": False})\n", + "\n", + "\n", + "w_auto = widgets.Layout(width=\"auto\")\n", + "data_opts = [\"Open\", \"Close\", \"High\", \"Low\", \"Volume\"]\n", + "data_widget = widgets.Dropdown(\n", + " options=data_opts, value=\"Close\", layout=w_auto, description=\"Data\"\n", + ")\n", + "\n", + "base_date = (datetime.today() - timedelta(days=365)).date()\n", + "start_widget = widgets.DatePicker(value=base_date, layout=w_auto, description=\"Start\")\n", + "end_widget = widgets.DatePicker(\n", + " value=datetime.today().date(), layout=w_auto, description=\"End\"\n", + ")\n", + "interval_widget = widgets.Dropdown(\n", + " options=interval_opts, value=\"1d\", layout=w_auto, description=\"Interval\"\n", + ")\n", + "tickers_widget = widgets.Textarea(value=\"TSLA,AAPL,\", layout=w_auto)\n", + "\n", + "data_box = widgets.VBox([tickers_widget, data_widget])\n", + "date_box = widgets.VBox([start_widget, end_widget, interval_widget])\n", + "controls = widgets.HBox([data_box, date_box], layout=widgets.Layout(width=\"60%\"))\n", + "\n", + "chart = Chart()\n", + "stocks_view = widgets.interactive_output(\n", + " chart.create,\n", + " {\n", + " \"data\": data_widget,\n", + " \"start\": start_widget,\n", + " \"end\": end_widget,\n", + " \"interval\": interval_widget,\n", + " \"tickers\": tickers_widget,\n", + " },\n", + ")\n", + "\n", + "title_html = \"

Correlation Analysis Dashboard

\"\n", + "warning_html = '

Use a comma after EVERY stock typed.

'\n", + "app_contents = [\n", + " widgets.HTML(title_html),\n", + " controls,\n", + " widgets.HTML(warning_html),\n", + " stocks_view,\n", + "]\n", + "app = widgets.VBox(app_contents)\n", + "display(app)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37f46146-a206-4c25-b969-81c5abec4b1d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "metadata": { + "interpreter": { + "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/jupyter/dashboards/crypto.ipynb b/openbb_terminal/jupyter/dashboards/crypto.ipynb new file mode 100644 index 00000000000..7972cda6037 --- /dev/null +++ b/openbb_terminal/jupyter/dashboards/crypto.ipynb @@ -0,0 +1,160 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6e198e40-0c33-45b3-a79b-2d9ee9eff7b9", + "metadata": { + "tags": [] + }, + "source": [ + "# Cryptocurrency exchange rates to USD ($)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da655985-c9b7-492e-aefa-fbe3bc1865d5", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "\n", + "import ipywidgets as ipw\n", + "from IPython.display import display\n", + "import pandas as pd\n", + "\n", + "from openbb_terminal import api as openbb\n", + "\n", + "ipw.HTML(f\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcbbf867-af50-4d06-ae5c-7c09647aa55d", + "metadata": {}, + "outputs": [], + "source": [ + "COIN_LIST = (\n", + " \"BTC,ETH,USDT,BNB,USDC,XRP,LUNA,ADA,\"\n", + " + \"SOL,AVAX,DOT,BUSD,DOGE,UST,SHIB,WBTC,\"\n", + " + \"MATIC,CRO,DAI,LTC,ATOM,NEAR,LINK,BCH,\"\n", + " + \"UNI,TRX,FTT,ETC,LEO,ALGO,XLM,MANA,\"\n", + " + \"BTCB,HBAR,EGLD,ICP,SAND,XMR,WAVES,VET,\"\n", + " + \"APE,FIL,FTM,AXS,THETA,KLAY,XTZ,RUNE\"\n", + ").split(\",\")\n", + "exchange_rates = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "deeeb80a-3675-44fb-9a1b-2325d8da4e7d", + "metadata": {}, + "outputs": [], + "source": [ + "def get_exchange_rate(coin: str, days: int = 2):\n", + " \"\"\"Get exchange rate for a cryptocurrency agains USD.\"\"\"\n", + " (_, _, _, _, current_df, _,) = openbb.crypto.load(\n", + " coin=coin,\n", + " source=\"cp\",\n", + " days=days,\n", + " vs=\"usd\",\n", + " interval=\"1day\",\n", + " should_load_ta_data=True,\n", + " )\n", + " price_color = \"neutral_color\"\n", + " if current_df[\"Close\"].iloc[-1] > current_df[\"Close\"].iloc[-2]:\n", + " price_color = \"up_color\"\n", + " elif current_df[\"Close\"].iloc[-1] < current_df[\"Close\"].iloc[-2]:\n", + " price_color = \"down_color\"\n", + " price = str(current_df[\"Close\"].iloc[-1])[:7]\n", + "\n", + " return price, price_color" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18acf5cb-6cbf-484d-8042-107c030098de", + "metadata": {}, + "outputs": [], + "source": [ + "def show_rates(btn):\n", + " \"\"\"Show exchange rates in grid box widget.\"\"\"\n", + " grid.children = ()\n", + " for coin in COIN_LIST:\n", + " if coin not in exchange_rates.keys():\n", + " exchange_rates[coin] = {\"price\": None, \"color\": None}\n", + " try:\n", + " price, price_color = get_exchange_rate(coin=coin)\n", + " except:\n", + " price, price_color = \"-------\", \"neutral_color\"\n", + " widgets = list(grid.children)\n", + " widgets.append(\n", + " ipw.HTML(\n", + " openbb.widgets.price_card(\n", + " ticker=coin,\n", + " price=price,\n", + " price_color=price_color,\n", + " )\n", + " )\n", + " )\n", + " grid.children = tuple(widgets)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf2021e9-de8a-466e-b341-fcd6abda64b5", + "metadata": {}, + "outputs": [], + "source": [ + "update_rates = ipw.Button(description=\"Update rates\")\n", + "update_rates.on_click(show_rates)\n", + "header = ipw.HBox([update_rates])\n", + "\n", + "layout = ipw.Layout(grid_template_columns=\"1fr \" * 8)\n", + "grid = ipw.GridBox(\n", + " [],\n", + " layout=layout,\n", + ")\n", + "\n", + "display(header, grid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19dfcbfe-eaec-406e-bb3c-72d1b3538a6e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "voila": { + "theme": "dark" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/jupyter/dashboards/currencies.ipynb b/openbb_terminal/jupyter/dashboards/currencies.ipynb new file mode 100644 index 00000000000..ad612c6ce8e --- /dev/null +++ b/openbb_terminal/jupyter/dashboards/currencies.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "da655985-c9b7-492e-aefa-fbe3bc1865d5", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import time\n", + "from datetime import datetime, timedelta\n", + "from typing import Optional\n", + "\n", + "import ipywidgets as ipw\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib_inline.backend_inline\n", + "from ipyflex import FlexLayout\n", + "from widget_helpers import price_card, stylesheet\n", + "\n", + "from openbb_terminal import api as openbb\n", + "\n", + "%matplotlib widget\n", + "openbb.theme.applyMPLstyle()\n", + "ipw.HTML(f\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5905453-4de6-4f5f-8012-9acdfca50987", + "metadata": {}, + "outputs": [], + "source": [ + "def get_exchange_rate(currency_pair: str) -> str:\n", + " \"\"\"Get exchange rate for a currency pair.\"\"\"\n", + " from_symbol, to_symbol = currency_pair.split(\"/\")\n", + " exchange_data = openbb.forex.models.av.get_quote(\n", + " to_symbol=to_symbol, from_symbol=from_symbol\n", + " )\n", + " return exchange_data[\"Realtime Currency Exchange Rate\"][\"5. Exchange Rate\"][:6]\n", + "\n", + "\n", + "def get_candle_widget(\n", + " output: Optional[ipw.Output],\n", + " to_symbol: str,\n", + " from_symbol: str,\n", + ") -> ipw.Output:\n", + " \"\"\"Plot a candle chart for a currency pair.\"\"\"\n", + " start_date = datetime.now() - timedelta(days=1)\n", + " start_date = start_date.strftime(\"%Y-%m-%d\")\n", + "\n", + " exchange_rate_data = openbb.forex.models.av.get_historical(\n", + " to_symbol=to_symbol,\n", + " from_symbol=from_symbol,\n", + " start_date=start_date,\n", + " resolution=\"i\",\n", + " interval=\"15\",\n", + " )\n", + " fig, ax = plt.subplots(1, 1, figsize=(11, 4))\n", + " openbb.forex.candle(\n", + " data=exchange_rate_data,\n", + " to_symbol=to_symbol,\n", + " from_symbol=from_symbol,\n", + " external_axes=[ax],\n", + " )\n", + "\n", + " ax.set_xlim(0, len(exchange_rate_data.index))\n", + " fig.canvas.header_visible = False\n", + " fig.canvas.footer_visible = False\n", + " with output:\n", + " output.clear_output(True)\n", + " fig.canvas.show()\n", + " return output\n", + "\n", + "\n", + "def on_dropdown_change(change):\n", + " \"\"\"Update charts on change of dropdown selection.\"\"\"\n", + " if change[\"type\"] == \"change\" and change[\"name\"] == \"value\":\n", + " output = ipw.Output()\n", + " get_candle_widget(\n", + " to_symbol=to_widget.value,\n", + " from_symbol=from_widget.value,\n", + " )\n", + " dashboard.children[\"Candle\"] = output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c768d15-37ad-441b-b069-506c4bdbb9a7", + "metadata": {}, + "outputs": [], + "source": [ + "exchange_rates = {\n", + " \"EUR/USD\": {\"latest\": None, \"previous\": None},\n", + " \"USD/JPY\": {\"latest\": None, \"previous\": None},\n", + " \"GBP/USD\": {\"latest\": None, \"previous\": None},\n", + " \"AUD/USD\": {\"latest\": None, \"previous\": None},\n", + " \"USD/CAD\": {\"latest\": None, \"previous\": None},\n", + " # \"USD/CNY\": {\"latest\": None, \"previous\": None},\n", + " # \"USD/CHF\": {\"latest\": None, \"previous\": None},\n", + " # \"USD/HKD\": {\"latest\": None, \"previous\": None},\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4968ab89-aafe-4234-933c-963bd672715d", + "metadata": {}, + "outputs": [], + "source": [ + "def compose_widgets():\n", + " widgets = {}\n", + " for currency_pair in exchange_rates:\n", + " price = exchange_rates[currency_pair][\"latest\"]\n", + " price_color = \"neutral_color\"\n", + " if exchange_rates[currency_pair][\"previous\"] is not None:\n", + " if (\n", + " exchange_rates[currency_pair][\"latest\"]\n", + " > exchange_rates[currency_pair][\"previous\"]\n", + " ):\n", + " price_color = \"up_color\"\n", + " elif (\n", + " exchange_rates[currency_pair][\"latest\"]\n", + " < exchange_rates[currency_pair][\"previous\"]\n", + " ):\n", + " price_color = \"down_color\"\n", + " widgets[currency_pair] = ipw.HTML(\n", + " price_card(ticker=currency_pair, price=price, price_color=price_color)\n", + " )\n", + " return widgets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f35d1500-a411-4cff-94ee-d65d4d1810f9", + "metadata": {}, + "outputs": [], + "source": [ + "# for currency_code in rates_to_usd:\n", + "# rates_to_usd[currency_code][\"previous\"] = rates_to_usd[currency_code][\"latest\"]\n", + "# rates_to_usd[currency_code][\"latest\"] = get_rate_against_usd(currency_code)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a72837b-4ddd-4c01-a493-ebb430202cbc", + "metadata": {}, + "outputs": [], + "source": [ + "widgets = compose_widgets()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c636543c-42fe-4dd6-a900-1552a3c28c7e", + "metadata": {}, + "outputs": [], + "source": [ + "currency_list = openbb.forex.models.av.get_currency_list()\n", + "from_widget = ipw.Dropdown(\n", + " options=currency_list,\n", + " value=\"EUR\",\n", + " description=\"From:\",\n", + " disabled=False,\n", + " layout=ipw.Layout(margin=\"130\"),\n", + ")\n", + "to_widget = ipw.Dropdown(\n", + " options=currency_list,\n", + " value=\"USD\",\n", + " description=\"To:\",\n", + " disabled=False,\n", + ")\n", + "exchange_selection = ipw.VBox([from_widget, to_widget])\n", + "\n", + "widgets[\"Select\"] = exchange_selection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d069845-d622-4878-8b18-b385c6cb7a7f", + "metadata": {}, + "outputs": [], + "source": [ + "output = ipw.Output()\n", + "output = get_candle_widget(\n", + " output=output,\n", + " to_symbol=to_widget.value,\n", + " from_symbol=from_widget.value,\n", + ")\n", + "widgets[\"Candle\"] = output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81c7e5c7-2e52-45ba-bf7d-aa240f95be2d", + "metadata": {}, + "outputs": [], + "source": [ + "from_widget.observe(on_dropdown_change)\n", + "to_widget.observe(on_dropdown_change)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0c7d921-e5dc-4ce8-97f1-a59b414a676a", + "metadata": {}, + "outputs": [], + "source": [ + "dashboard = FlexLayout(\n", + " layout_config={\"borderLeft\": False, \"borderRight\": False, \"enableSection\": False},\n", + " style={\n", + " \"height\": \"calc(100vh - 80px)\",\n", + " \"backgroundColor\": \"rgb(0 0 0)\",\n", + " \"fontFamily\": \"Consolas\",\n", + " \"fontWeight\": 800,\n", + " },\n", + " header={\n", + " \"title\": \"Currencies\",\n", + " \"style\": {\n", + " \"backgroundColor\": \"rgb(0 0 0)\",\n", + " \"fontWeight\": 400,\n", + " \"fontSize\": \"28px\",\n", + " },\n", + " \"buttons\": [],\n", + " },\n", + " widgets=widgets,\n", + " template=os.path.join(\"widgets\", \"currencies.json\"),\n", + " editable=False,\n", + ")\n", + "dashboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5fedac44-5e09-44f0-bbbe-fbad7c8716cd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c850ba2-408b-4061-947e-5a1b8a284d22", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "voila": { + "theme": "dark" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/jupyter/dashboards/shortdata.ipynb b/openbb_terminal/jupyter/dashboards/shortdata.ipynb new file mode 100644 index 00000000000..04e1791cfb2 --- /dev/null +++ b/openbb_terminal/jupyter/dashboards/shortdata.ipynb @@ -0,0 +1,255 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [ + "import io\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import requests\n", + "from datetime import datetime, timedelta\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "%matplotlib widget" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "class ShortDataDash:\n", + " def __init__(self):\n", + " self.df = pd.DataFrame()\n", + " self.days_slider = widgets.IntSlider(\n", + " value=5,\n", + " min=5,\n", + " max=252,\n", + " step=1,\n", + " description=\"Days Back\",\n", + " style={\"description_width\": \"initial\"},\n", + " )\n", + " self.count_slider = widgets.IntSlider(\n", + " value=10,\n", + " min=1,\n", + " max=25,\n", + " step=1,\n", + " description=\"Number to show.\",\n", + " style={\"description_width\": \"initial\"},\n", + " )\n", + " self.output1 = widgets.Output()\n", + " self.output2 = widgets.Output()\n", + " self.load_button = widgets.Button(\n", + " description=\"Load Data\", layout=widgets.Layout(width=\"200px\", height=\"40px\")\n", + " )\n", + " self.load_button.on_click(self.load_button_click)\n", + " self.show_button = widgets.Button(\n", + " description=\"Change Number Shown\", layout=self.load_button.layout\n", + " )\n", + " self.show_button.on_click(self.show_button_click)\n", + "\n", + " self.slider_box = widgets.HBox([self.days_slider, self.count_slider])\n", + " self.button_box = widgets.VBox([self.load_button, self.show_button])\n", + "\n", + " self.stock_input = widgets.Text(\n", + " value=\"GME\",\n", + " placeholder=\"GME\",\n", + " description=\"Ticker:\",\n", + " )\n", + " self.ticker_button = widgets.Button(description=\"Plot Ticker\")\n", + " self.ticker_button.on_click(self.ticker_button_click)\n", + "\n", + " def show_button_click(self, b):\n", + " self.output1.clear_output()\n", + " with self.output1:\n", + " self.update()\n", + "\n", + " def load_button_click(self, b):\n", + " self.output1.clear_output()\n", + " self.output2.clear_output()\n", + " with self.output1:\n", + " print(f\"Data Loading for {self.days_slider.value} days\")\n", + " self.fetch_new_data()\n", + " self.update()\n", + "\n", + " def ticker_button_click(self, b):\n", + " self.output2.clear_output()\n", + " with self.output2:\n", + " self.ticker_plot()\n", + "\n", + " def fetch_new_data(self):\n", + " self.df = pd.DataFrame()\n", + " today = datetime.now().date()\n", + " idx = 0\n", + " len_df = 0\n", + " while len_df < self.days_slider.value:\n", + " date = today - timedelta(days=idx)\n", + " r = requests.get(\n", + " f\"https://cdn.finra.org/equity/regsho/daily/CNMSshvol{date.strftime('%Y%m%d')}.txt\"\n", + " )\n", + " if r.status_code == 200:\n", + " self.df = pd.concat(\n", + " [self.df, pd.read_csv(io.StringIO(r.text), sep=\"|\")], axis=0\n", + " )\n", + " len_df += 1\n", + " idx += 1\n", + " self.df = self.df[self.df.Date > 20100101]\n", + " self.df.Date = self.df[\"Date\"].apply(\n", + " lambda x: datetime.strptime(str(x), \"%Y%m%d\")\n", + " )\n", + "\n", + " def update(self):\n", + " if not self.df.empty:\n", + " temp = (\n", + " self.df.groupby(\"Symbol\")[[\"ShortVolume\", \"TotalVolume\"]]\n", + " .agg(\"sum\")\n", + " .sort_values(by=\"ShortVolume\", ascending=False)\n", + " .head(self.count_slider.value)[::-1]\n", + " )\n", + " self.fig, self.ax = plt.subplots(figsize=(6, 6))\n", + " self.ax.barh(temp.index, temp.TotalVolume, alpha=0.4, label=\"Total Volume\")\n", + " self.ax.barh(temp.index, temp.ShortVolume, label=\"Short Volume\")\n", + " self.ax.set_title(\n", + " f\"Top {self.count_slider.value} Short Volume in Last {self.days_slider.value} Days\"\n", + " )\n", + " self.ax.legend()\n", + " self.fig.tight_layout()\n", + " plt.show()\n", + "\n", + " def ticker_plot(self):\n", + " stock_data = self.df.copy().loc[\n", + " self.df.Symbol == self.stock_input.value,\n", + " [\"Date\", \"ShortVolume\", \"TotalVolume\"],\n", + " ]\n", + " self.fig2, self.ax2 = plt.subplots(figsize=(6, 6))\n", + " self.ax2.plot(\n", + " stock_data.Date, stock_data.TotalVolume, alpha=0.4, label=\"Total Volume\"\n", + " )\n", + " self.ax2.plot(stock_data.Date, stock_data.ShortVolume, label=\"Short Volume\")\n", + " self.ax2.set_title(\n", + " f\"Stock Volume and Short Volume for {self.stock_input.value.upper()}\"\n", + " )\n", + " self.ax2.legend()\n", + " self.fig2.autofmt_xdate()\n", + " self.fig2.tight_layout()\n", + " plt.show()\n", + "\n", + " def build_app(self):\n", + " title_html = \"\"\"\n", + "

Finra Short Data

\n", + "

This widget downloads the consolidated NMS short data from FINRA and aggregates the data by summing over the entire time period.

\n", + "

Note that clicking the 'Load Data' button will reload all data. This can get time consuming, so if you pick a few hundred days, expect a few minutes for loading time.

\n", + "\"\"\"\n", + " middle_html = \"\"\"\n", + "Here we allow the user to query for a single stock. This will work with the loaded data. Note that if you want to reload the data, this will once again take some time.\n", + " \"\"\"\n", + " return [\n", + " widgets.HTML(\n", + " title_html, layout=widgets.Layout(margin=\"0 0 3em 0\", max_width=\"800px\")\n", + " ),\n", + " self.slider_box,\n", + " self.button_box,\n", + " self.output1,\n", + " widgets.HTML(\n", + " middle_html,\n", + " layout=widgets.Layout(margin=\"0 0 3em 0\", max_width=\"800px\"),\n", + " ),\n", + " self.stock_input,\n", + " self.ticker_button,\n", + " self.output2,\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "dash = ShortDataDash()\n", + "app = widgets.VBox(\n", + " dash.build_app(), layout=widgets.Layout(max_width=\"1024px\", margin=\"0 auto 0 auto\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "dcda08c8d643465fb862d01341d71370", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value=\"\\n

Finra Short Data

\\n

This widget downloads the consolidated NMS short da…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(app)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/openbb_terminal/jupyter/dashboards/stocks.ipynb b/openbb_terminal/jupyter/dashboards/stocks.ipynb new file mode 100644 index 00000000000..dcdafd3df9c --- /dev/null +++ b/openbb_terminal/jupyter/dashboards/stocks.ipynb @@ -0,0 +1,404 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "e9ad60bc", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from datetime import datetime, timedelta\n", + "import ipywidgets as widgets\n", + "import plotly.graph_objs as go\n", + "import yfinance as yf\n", + "import pandas as pd\n", + "from IPython.display import display" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "beb36881", + "metadata": {}, + "outputs": [], + "source": [ + "interval_opts = [\n", + " \"1m\",\n", + " \"2m\",\n", + " \"5m\",\n", + " \"15m\",\n", + " \"30m\",\n", + " \"60m\",\n", + " \"90m\",\n", + " \"1h\",\n", + " \"1d\",\n", + " \"5d\",\n", + " \"1wk\",\n", + " \"1mo\",\n", + " \"3mo\",\n", + "]\n", + "\n", + "rows = [\n", + " \"sector\",\n", + " \"marketCap\",\n", + " \"beta\",\n", + " \"fiftyTwoWeekHigh\",\n", + " \"fiftyTwoWeekLow\",\n", + " \"floatShares\",\n", + " \"sharesShort\",\n", + " \"exDividendDate\",\n", + "]\n", + "\n", + "views = {\n", + " \"Raw Data\": lambda x, y: x,\n", + " \"Percent Change\": lambda x, y: x.pct_change(),\n", + " \"Rolling Average\": lambda x, y: x.rolling(y).mean(),\n", + " \"Rolling Variance\": lambda x, y: x.rolling(y).var(),\n", + " \"Rolling Standard Deviation\": lambda x, y: x.rolling(y).var() ** 0.5,\n", + " \"Rolling Coefficient of Variation\": lambda x, y: (x.rolling(y).var() ** 0.5)\n", + " / (x.rolling(y).mean()),\n", + "}\n", + "\n", + "clean_row = {\n", + " \"sector\": \"Sector\",\n", + " \"marketCap\": \"M Cap\",\n", + " \"beta\": \"Beta\",\n", + " \"fiftyTwoWeekHigh\": \"52W High\",\n", + " \"fiftyTwoWeekLow\": \"52W Low\",\n", + " \"floatShares\": \"Floats\",\n", + " \"sharesShort\": \"Shorts\",\n", + " \"exDividendDate\": \"Ex-Div\",\n", + "}\n", + "\n", + "clean_data = {\n", + " \"sector\": lambda x: \"N/A\" if x is None else x,\n", + " \"marketCap\": lambda x: \"N/A\" if x is None else big_num(x),\n", + " \"beta\": lambda x: \"N/A\" if x is None else f\"{round(x,2)}\",\n", + " \"fiftyTwoWeekHigh\": lambda x: \"N/A\" if x is None else f\"${round(x,2)}\",\n", + " \"fiftyTwoWeekLow\": lambda x: \"N/A\" if x is None else f\"${round(x,2)}\",\n", + " \"floatShares\": lambda x: \"N/A\" if x is None else big_num(x),\n", + " \"sharesShort\": lambda x: \"N/A\" if x is None else big_num(x),\n", + " \"exDividendDate\": lambda x: \"N/A\"\n", + " if x is None\n", + " else datetime.fromtimestamp(x).strftime(\"%Y/%m/%d\"),\n", + "}\n", + "\n", + "\n", + "def big_num(num):\n", + " if num > 1_000_000_000_000:\n", + " return f\"{round(num/1_000_000_000_000,2)}T\"\n", + " if num > 1_000_000_000:\n", + " return f\"{round(num/1_000_000_000,2)}B\"\n", + " if num > 1_000_000:\n", + " return f\"{round(num/1_000_000,2)}M\"\n", + " if num > 1_000:\n", + " return f\"{num/round(1_000,2)}K\"\n", + " return f\"{round(num,2)}\"\n", + "\n", + "\n", + "def clean_str(string):\n", + " new_str = \"\"\n", + " for letter in string:\n", + " if letter.isupper():\n", + " new_str += \" \"\n", + " new_str += letter\n", + " return new_str.title()\n", + "\n", + "\n", + "def format_plotly(fig, data, start, end, chart, calc=None):\n", + " fig.update_yaxes(title=None)\n", + " fig.update_xaxes(title=None)\n", + " start_t = start.strftime(\"%Y/%m/%d\")\n", + " end_t = end.strftime(\"%Y/%m/%d\")\n", + " if calc:\n", + " if len(calc) == 1:\n", + " fig_title = f\"{calc[0]} of {data} from {start_t} to {end_t}\"\n", + " else:\n", + " fig_title = f\"{', '.join(calc)} of {data} from {start_t} to {end_t}\"\n", + " else:\n", + " fig_title = \"Volume\"\n", + " height = 500 if chart == \"main\" else 300\n", + " fig.update_layout(\n", + " margin=dict(l=0, r=10, t=10, b=10),\n", + " autosize=False,\n", + " width=900,\n", + " height=height,\n", + " legend=dict(orientation=\"h\"),\n", + " title={\n", + " \"text\": fig_title,\n", + " \"y\": 0.95,\n", + " \"x\": 0.5,\n", + " \"xanchor\": \"center\",\n", + " \"yanchor\": \"top\",\n", + " },\n", + " )\n", + "\n", + "\n", + "def create_line(visual, x, y, name, data, fig):\n", + " if visual == \"line\":\n", + " plot = go.Scatter(x=x, y=y[data], mode=\"lines\", name=name, connectgaps=True)\n", + " if visual == \"scatter\":\n", + " plot = go.Scatter(x=x, y=y[data], mode=\"markers\", name=name)\n", + " if visual == \"candle\":\n", + " plot = go.Candlestick(\n", + " x=x,\n", + " open=y[\"Open\"],\n", + " close=y[\"Close\"],\n", + " high=y[\"High\"],\n", + " low=y[\"Low\"],\n", + " name=name,\n", + " )\n", + " fig.add_trace(plot)\n", + "\n", + "\n", + "def show_fig(fig):\n", + " config = {\"showTips\": False, \"scrollZoom\": True}\n", + " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", + " fig.show(config=config, renderer=\"notebook\")\n", + " else:\n", + " fig.show(config=config)\n", + "\n", + "\n", + "def table_data(infos):\n", + " cols = [\"Ticker\"] + list(infos)\n", + " data = pd.DataFrame(columns=cols)\n", + " data[\"Ticker\"] = [clean_row[x] for x in rows]\n", + " for ticker in list(infos):\n", + " data[ticker] = [clean_data[x](infos[ticker].get(x, None)) for x in rows]\n", + " new_cols = {k: clean_str(k) for k in rows}\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8c2f65c", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "class Chart:\n", + " def __init__(self):\n", + " self.last_tickers = \"\"\n", + " self.last_interval = \"1d\"\n", + " self.df = pd.DataFrame()\n", + " self.infos = {}\n", + "\n", + " def create_stock(\n", + " self, calculation, data, rolling, start, end, interval, tickers, chart\n", + " ):\n", + " if tickers and tickers[-1] == \",\":\n", + " if tickers != self.last_tickers or interval != self.last_interval:\n", + " if interval in [\"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"]:\n", + " self.df = yf.download(\n", + " tickers, period=\"max\", interval=interval, progress=False\n", + " )\n", + " else:\n", + " end_date = end + timedelta(days=1)\n", + " self.df = yf.download(\n", + " tickers,\n", + " start=start,\n", + " end=end_date,\n", + " interval=interval,\n", + " progress=False,\n", + " )\n", + " self.last_tickers = tickers\n", + " self.last_interval = interval\n", + "\n", + " start_n = datetime(start.year, start.month, start.day)\n", + " end_n = datetime(end.year, end.month, end.day)\n", + " fig = go.Figure()\n", + " for item in calculation:\n", + " calcs = views[item](self.df, rolling)\n", + " if interval in [\"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"]:\n", + " result = calcs.loc[\n", + " (calcs.index >= start_n) & (calcs.index <= end_n)\n", + " ]\n", + " else:\n", + " result = calcs\n", + "\n", + " if len(result.columns) == 6:\n", + " name = f\"{tickers.split(',')[0]} {item}\"\n", + " create_line(chart, result.index, result, name, data, fig)\n", + "\n", + " else:\n", + " for val in result.columns.levels[1]:\n", + " vals = result.xs(val, axis=1, level=1, drop_level=True)\n", + " name = f\"{val.upper()} {item}\"\n", + " create_line(chart, result.index, vals, name, data, fig)\n", + "\n", + " format_plotly(fig, data, start, end, \"main\", calculation)\n", + " show_fig(fig)\n", + "\n", + " def create_volume(self, start, end, interval, tickers):\n", + " start_n = datetime(start.year, start.month, start.day)\n", + " end_n = datetime(end.year, end.month, end.day)\n", + " result = self.df.loc[(self.df.index >= start_n) & (self.df.index <= end_n)]\n", + " fig = go.Figure()\n", + " if len(result.columns) == 6:\n", + " name = f\"{tickers.split(',')[0]}\"\n", + " create_line(\"line\", result.index, result, name, \"Volume\", fig)\n", + " else:\n", + " for val in result.columns.levels[1]:\n", + " vals = result.xs(val, axis=1, level=1, drop_level=True)\n", + " name = f\"{val.upper()}\"\n", + " create_line(\"line\", result.index, vals, name, \"Volume\", fig)\n", + " format_plotly(fig, \"Volume\", start, end, \"volume\")\n", + " show_fig(fig)\n", + "\n", + " def create_table(self, tickers):\n", + " if tickers and tickers[-1] == \",\":\n", + " clean_tickers = [x for x in tickers.split(\",\") if x]\n", + " for ticker in clean_tickers:\n", + " if ticker not in self.infos:\n", + " self.infos[ticker] = yf.Ticker(ticker).info\n", + " for ticker in self.infos:\n", + " if ticker not in tickers:\n", + " self.infos.pop(ticker)\n", + " result = table_data(self.infos)\n", + " fig = go.Figure(\n", + " data=[\n", + " go.Table(\n", + " header=dict(\n", + " values=result.columns,\n", + " fill_color=\"lightgray\",\n", + " font=dict(color=\"black\"),\n", + " align=\"left\",\n", + " ),\n", + " cells=dict(\n", + " values=[result[x] for x in result.columns],\n", + " font=dict(color=\"black\"),\n", + " align=\"left\",\n", + " ),\n", + " )\n", + " ],\n", + " )\n", + " fig.update_layout(margin=dict(l=0, r=20, t=0, b=0), width=350)\n", + " show_fig(fig)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0d4de31-1b4f-4c1f-8467-ca314a578d56", + "metadata": {}, + "outputs": [], + "source": [ + "w_auto = widgets.Layout(width=\"auto\")\n", + "calc_widget = widgets.SelectMultiple(\n", + " options=list(views.keys()), value=[\"Raw Data\"], layout=w_auto\n", + ")\n", + "\n", + "data_opts = [\"Open\", \"Close\", \"High\", \"Low\"]\n", + "data_widget = widgets.Dropdown(\n", + " options=data_opts, value=\"Close\", layout=w_auto, description=\"Data\"\n", + ")\n", + "rolling_widget = widgets.Dropdown(\n", + " options=list(range(2, 101)), value=60, layout=w_auto, description=\"Rolling\"\n", + ")\n", + "\n", + "base_date = (datetime.today() - timedelta(days=365)).date()\n", + "start_widget = widgets.DatePicker(value=base_date, layout=w_auto, description=\"Start\")\n", + "end_widget = widgets.DatePicker(\n", + " value=datetime.today().date(), layout=w_auto, description=\"End\"\n", + ")\n", + "\n", + "interval_widget = widgets.Dropdown(\n", + " options=interval_opts, value=\"1d\", layout=w_auto, description=\"Interval\"\n", + ")\n", + "tickers_widget = widgets.Textarea(\n", + " value=\"TSLA,\", layout=widgets.Layout(width=\"auto\", height=\"100%\")\n", + ")\n", + "chart_opts = [\"line\", \"scatter\", \"candle\"]\n", + "chart_widget = widgets.Dropdown(\n", + " options=chart_opts, value=\"line\", layout=w_auto, description=\"Chart\"\n", + ")\n", + "data_box = widgets.VBox([data_widget, rolling_widget, chart_widget])\n", + "date_box = widgets.VBox([start_widget, end_widget, interval_widget])\n", + "controls = widgets.HBox(\n", + " [tickers_widget, calc_widget, date_box, data_box],\n", + " layout=widgets.Layout(width=\"90%\"),\n", + ")\n", + "chart = Chart()\n", + "stocks_view = widgets.interactive_output(\n", + " chart.create_stock,\n", + " {\n", + " \"calculation\": calc_widget,\n", + " \"data\": data_widget,\n", + " \"rolling\": rolling_widget,\n", + " \"start\": start_widget,\n", + " \"end\": end_widget,\n", + " \"interval\": interval_widget,\n", + " \"tickers\": tickers_widget,\n", + " \"chart\": chart_widget,\n", + " },\n", + ")\n", + "\n", + "volume_view = widgets.interactive_output(\n", + " chart.create_volume,\n", + " {\n", + " \"start\": start_widget,\n", + " \"end\": end_widget,\n", + " \"interval\": interval_widget,\n", + " \"tickers\": tickers_widget,\n", + " },\n", + ")\n", + "\n", + "table_view = widgets.interactive_output(chart.create_table, {\"tickers\": tickers_widget})\n", + "\n", + "charts = widgets.VBox(\n", + " [stocks_view, volume_view],\n", + " layout=widgets.Layout(width=\"100%\", padding=\"0\", margin=\"0\"),\n", + ")\n", + "figures = widgets.HBox(\n", + " [charts, table_view], layout=widgets.Layout(padding=\"0\", margin=\"0\")\n", + ")\n", + "\n", + "title_html = \"

Stock Analysis Dashboard

\"\n", + "warning_html = '

Use a comma after EVERY stock typed.

'\n", + "\n", + "app_contents = [widgets.HTML(title_html), controls, widgets.HTML(warning_html), figures]\n", + "app = widgets.VBox(app_contents)\n", + "display(app)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f33793c-adc9-4bc1-8fd4-aeff5a00ccba", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "metadata": { + "interpreter": { + "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/jupyter/dashboards/vsurf.ipynb b/openbb_terminal/jupyter/dashboards/vsurf.ipynb new file mode 100644 index 00000000000..eff046fbefa --- /dev/null +++ b/openbb_terminal/jupyter/dashboards/vsurf.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "68b664c7-c52d-42c1-b170-575370323d22", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "import sys\n", + "import ipywidgets as widgets\n", + "import plotly.graph_objects as go\n", + "import numpy as np\n", + "import pandas as pd\n", + "from IPython.display import display\n", + "from openbb_terminal import api as openbb\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "module_path = os.path.abspath(os.path.join(\"../..\"))\n", + "\n", + "if module_path not in sys.path:\n", + " sys.path.append(module_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c98dc75-6325-43da-93a2-1b8ec96bd368", + "metadata": {}, + "outputs": [], + "source": [ + "z_dict = {\n", + " \"IV\": \"Implied Volatility\",\n", + " \"OI\": \"Open Interest\",\n", + " \"LP\": \"Last Price\",\n", + "}\n", + "strings = {\n", + " \"IV\": \"DTE: %{x}
Strike: %{y}
IV: %{z}\",\n", + " \"OI\": \"DTE: %{x}
Strike: %{y}
OI: %{z}\",\n", + " \"LP\": \"DTE: %{x}
Strike: %{y}
LP: %{z}\",\n", + "}\n", + "\n", + "\n", + "def create_layout(fig, width, height, z, ticker):\n", + " fig.update_layout(\n", + " title=f\"{z.upper()} Surface for {ticker.upper()}\",\n", + " autosize=False,\n", + " width=width,\n", + " height=height,\n", + " scene=dict(\n", + " xaxis_title=\"Days to Expiration\",\n", + " yaxis_title=\"Strike\",\n", + " zaxis_title=z_dict[z],\n", + " ),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1bc5c6a-ccb2-4ef5-9c79-1f619fbf95a6", + "metadata": {}, + "outputs": [], + "source": [ + "class Chart:\n", + " def __init__(self):\n", + " self.last_ticker = \"\"\n", + " self.df = pd.DataFrame()\n", + "\n", + " def create(self, z, ticker, height, width):\n", + " if ticker:\n", + " if ticker != self.last_ticker:\n", + " self.df = openbb.stocks.options.models.yfinance.get_iv_surface(ticker)\n", + " self.last_ticker = ticker\n", + "\n", + " if not self.df.empty:\n", + " z_dict = {\n", + " \"IV\": self.df.impliedVolatility,\n", + " \"OI\": self.df.openInterest,\n", + " \"LP\": self.df.lastPrice,\n", + " }\n", + " fig = go.Figure(\n", + " data=[\n", + " go.Mesh3d(\n", + " z=z_dict[z],\n", + " x=self.df.dte,\n", + " y=self.df.strike,\n", + " intensity=np.sqrt(z_dict[z]),\n", + " colorscale=\"Jet\",\n", + " hovertemplate=strings[z],\n", + " showscale=False,\n", + " )\n", + " ]\n", + " )\n", + "\n", + " create_layout(fig, height, width, z, ticker)\n", + " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", + " fig.show(config={\"showTips\": False}, renderer=\"notebook\")\n", + " else:\n", + " fig.show(config={\"showTips\": False})\n", + "\n", + "\n", + "opts = [\"IV\", \"OI\", \"LP\"]\n", + "z_widget = widgets.Dropdown(options=opts, value=\"IV\", description=\"Data\")\n", + "tickers_widget = widgets.Text(description=\"Ticker\", value=\"TSLA\")\n", + "h_widget = widgets.IntText(value=1000, description=\"Height\")\n", + "w_widget = widgets.IntText(value=1000, description=\"Width\")\n", + "\n", + "controls = widgets.VBox([tickers_widget, z_widget])\n", + "size = widgets.VBox([h_widget, w_widget])\n", + "controls = widgets.HBox([controls, size])\n", + "\n", + "chart = Chart()\n", + "stocks_view = widgets.interactive_output(\n", + " chart.create,\n", + " {\"z\": z_widget, \"ticker\": tickers_widget, \"height\": h_widget, \"width\": w_widget},\n", + ")\n", + "\n", + "title_html = \"

Option Surface Dashboard

\"\n", + "app_contents = [\n", + " widgets.HTML(title_html),\n", + " controls,\n", + " stocks_view,\n", + "]\n", + "app = widgets.VBox(app_contents)\n", + "display(app)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "014f267a-92ba-4f23-afb1-9720d8a68cbb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/jupyter/reports/crypto_market.ipynb b/openbb_terminal/jupyter/reports/crypto_market.ipynb new file mode 100644 index 00000000000..41f571e9280 --- /dev/null +++ b/openbb_terminal/jupyter/reports/crypto_market.ipynb @@ -0,0 +1,273 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "elect-highway", + "metadata": {}, + "outputs": [], + "source": [ + "# Import most generic modules\n", + "import os\n", + "import sys\n", + "from datetime import datetime\n", + "from IPython.display import display, Markdown\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "module_path = os.path.abspath(os.path.join(\"../..\"))\n", + "\n", + "if module_path not in sys.path:\n", + " sys.path.append(module_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fatty-colony", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "report_name = f\"{datetime.now().strftime('%Y%m%d_%H%M%S')}_crypto_market\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "medieval-majority", + "metadata": {}, + "outputs": [], + "source": [ + "display(Markdown(f\"# Crypto Market - {datetime.now().strftime('%Y/%m/%d %H:%M:%S')}\"))" + ] + }, + { + "cell_type": "markdown", + "id": "basic-sucking", + "metadata": {}, + "source": [ + "## Bitcoin Chart" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "318022a0", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.cryptocurrency import cryptocurrency_helpers\n", + "\n", + "coin_map_df = (\n", + " cryptocurrency_helpers.prepare_all_coins_df().set_index(\"Symbol\").loc[\"btc\"]\n", + ")\n", + "cryptocurrency_helpers.plot_chart(\n", + " coin=\"btc-bitcoin\", source=\"cp\", currency=\"USD\", days=60, coin_map_df=coin_map_df\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "animal-titanium", + "metadata": {}, + "source": [ + "## Global Market Information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "artistic-stewart", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.cryptocurrency.overview import coinpaprika_view\n", + "\n", + "coinpaprika_view.display_global_market(export=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "twelve-visiting", + "metadata": {}, + "source": [ + "## Top coins market information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "photographic-payday", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.cryptocurrency.overview import coinpaprika_view\n", + "\n", + "coinpaprika_view.display_all_coins_market_info(\n", + " currency=\"USD\", sortby=\"volume_24h\", descend=True, top=10, export=\"\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "median-spirituality", + "metadata": {}, + "source": [ + "## Top gainers in last 24h" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "threaded-archives", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.cryptocurrency.discovery import pycoingecko_view\n", + "\n", + "pycoingecko_view.display_gainers(period=\"24h\", top=10, export=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "recognized-intent", + "metadata": {}, + "source": [ + "## Top losers in last 24h" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "exceptional-objective", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.cryptocurrency.discovery import pycoingecko_view\n", + "\n", + "pycoingecko_view.display_losers(period=\"24h\", top=10, export=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "rough-routine", + "metadata": {}, + "source": [ + "## Coins trending on coingecko" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "matched-syria", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.cryptocurrency.discovery import pycoingecko_view\n", + "\n", + "pycoingecko_view.display_trending(\n", + " export=\"\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "established-order", + "metadata": {}, + "source": [ + "## Decentralized Finance Global Market Info" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "artistic-grocery", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.cryptocurrency.overview import pycoingecko_view\n", + "\n", + "pycoingecko_view.display_global_defi_info(export=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "elect-universe", + "metadata": {}, + "source": [ + "## Top DeFi Coins" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "configured-macedonia", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.cryptocurrency.discovery import pycoingecko_view\n", + "\n", + "pycoingecko_view.display_coins(\"decentralized-finance-defi\", top=10, export=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "comparative-congo", + "metadata": {}, + "source": [ + "## Top Decentralized Exchanges" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "scientific-healthcare", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.cryptocurrency.discovery import dappradar_view\n", + "\n", + "dappradar_view.display_top_dexes(top=10, export=\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "knowing-imagination", + "metadata": {}, + "outputs": [], + "source": [ + "!jupyter nbconvert {report_name + '.ipynb'} --to html --no-input" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "interpreter": { + "hash": "158b8ab50e95ded652d8f2b468df64cac8eaa223c5e60f12cdaaeaccc6039b6d" + }, + "kernelspec": { + "display_name": "Python 3.8.12 64-bit ('gst': conda)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/jupyter/reports/dark_pool.ipynb b/openbb_terminal/jupyter/reports/dark_pool.ipynb new file mode 100644 index 00000000000..782c8707a8b --- /dev/null +++ b/openbb_terminal/jupyter/reports/dark_pool.ipynb @@ -0,0 +1,253 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "driven-billion", + "metadata": {}, + "outputs": [], + "source": [ + "# Import most generic modules\n", + "import importlib\n", + "import pathlib\n", + "import os\n", + "import sys\n", + "from datetime import datetime, timedelta\n", + "import pandas as pd\n", + "from IPython.display import display, Markdown\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "module_path = os.path.abspath(os.path.join(\"../..\"))\n", + "\n", + "if module_path not in sys.path:\n", + " sys.path.append(module_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "intended-sherman", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Parameters that will be replaced when calling this notebook\n", + "ticker = \"AMC\"\n", + "\n", + "report_name = f\"{datetime.now().strftime('%Y%m%d_%H%M%S')}_dark_pool_{ticker.upper()}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "mounted-phase", + "metadata": {}, + "outputs": [], + "source": [ + "display(\n", + " Markdown(\n", + " f\"# {ticker.upper()} - Dark Pool - {datetime.now().strftime('%Y/%m/%d %H:%M:%S')}\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "capable-allergy", + "metadata": {}, + "source": [ + "## Top 10 Negative Dark Pool Positions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "statewide-debate", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.dark_pool_shorts import stockgrid_view\n", + "\n", + "stockgrid_view.dark_pool_short_positions(\n", + " num=10, sort_field=\"dpp_dollar\", ascending=True, export=\"\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "artificial-tampa", + "metadata": {}, + "source": [ + "## Top 10 Most Highly Shorted Stocks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "smaller-association", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.dark_pool_shorts import shortinterest_view\n", + "\n", + "shortinterest_view.high_short_interest(num=10, export=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "cleared-contemporary", + "metadata": {}, + "source": [ + "## Dark Pool Activity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "medical-tomato", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.dark_pool_shorts import stockgrid_view\n", + "\n", + "stockgrid_view.net_short_position(ticker=ticker.upper(), num=60, raw=False, export=\"\")\n", + "\n", + "stockgrid_view.net_short_position(ticker=ticker.upper(), num=10, raw=True, export=\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "comprehensive-nashville", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.dark_pool_shorts import stockgrid_view\n", + "\n", + "stockgrid_view.short_interest_volume(\n", + " ticker=ticker.upper(), num=60, raw=False, export=\"\"\n", + ")\n", + "\n", + "stockgrid_view.short_interest_volume(ticker=ticker.upper(), num=10, raw=True, export=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "decreased-mills", + "metadata": {}, + "source": [ + "## Options info" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "medical-shaft", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.options import barchart_view\n", + "\n", + "barchart_view.print_options_data(ticker=ticker.upper(), export=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "understood-houston", + "metadata": {}, + "source": [ + "## Put and Call Open Interest Expiring next" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acute-burning", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.options import yfinance_model, yfinance_view\n", + "\n", + "selected_date = yfinance_model.option_expirations(ticker)[0]\n", + "options = yfinance_model.get_option_chain(ticker, selected_date)\n", + "\n", + "yfinance_view.plot_oi(\n", + " ticker=ticker,\n", + " expiry=selected_date,\n", + " min_sp=-1,\n", + " max_sp=-1,\n", + " calls_only=False,\n", + " puts_only=False,\n", + " export=\"\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "convinced-orleans", + "metadata": {}, + "source": [ + "## Failure-to-deliver (30 days lag)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "capital-narrow", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks import stocks_helper\n", + "from openbb_terminal.stocks.dark_pool_shorts import sec_view\n", + "\n", + "stock = stocks_helper.load(ticker)\n", + "\n", + "sec_view.fails_to_deliver(\n", + " ticker=ticker.upper(),\n", + " stock=stock,\n", + " start=datetime.now() - timedelta(days=60),\n", + " end=datetime.now(),\n", + " num=0,\n", + " raw=False,\n", + " export=\"\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "above-prior", + "metadata": {}, + "outputs": [], + "source": [ + "!jupyter nbconvert {report_name + \".ipynb\"} --to html --no-input" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "", + "language": "python", + "name": "" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/jupyter/reports/due_dilligence.ipynb b/openbb_terminal/jupyter/reports/due_dilligence.ipynb new file mode 100644 index 00000000000..728c5aac7b2 --- /dev/null +++ b/openbb_terminal/jupyter/reports/due_dilligence.ipynb @@ -0,0 +1,445 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "70ba2c8a", + "metadata": {}, + "source": [ + "## Notebook setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "driven-billion", + "metadata": {}, + "outputs": [], + "source": [ + "import io\n", + "import warnings\n", + "from datetime import datetime, timedelta\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib_inline.backend_inline\n", + "\n", + "from openbb_terminal import api as openbb\n", + "from openbb_terminal.helper_classes import TerminalStyle\n", + "\n", + "%matplotlib inline\n", + "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3fe7db5-ec6a-42cf-9e66-52dc1de22370", + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " theme = TerminalStyle(\"light\", \"light\", \"light\")\n", + "except:\n", + " pass\n", + "stylesheet = openbb.widgets.html_report_stylesheet()" + ] + }, + { + "cell_type": "markdown", + "id": "807fda4e", + "metadata": {}, + "source": [ + "## Select Ticker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "intended-sherman", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Parameters that will be replaced when calling this notebook\n", + "ticker = \"AMC\"\n", + "report_name = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1be26dae-cafe-4a22-80aa-eff296fc1a9b", + "metadata": {}, + "outputs": [], + "source": [ + "ticker_data = openbb.stocks.load(ticker, start=datetime.now() - timedelta(days=365))\n", + "ticker_data = openbb.stocks.process_candle(ticker_data)\n", + "\n", + "report_title = f\"{ticker.upper()} Due Diligence report ({datetime.now().strftime('%Y-%m-%d %H:%M:%S')})\"\n", + "report_title" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3b15d6f-dc9e-4f97-aa07-37d949a4e4bb", + "metadata": {}, + "outputs": [], + "source": [ + "overview = openbb.stocks.fa.models.yahoo_finance.get_info(ticker=ticker).transpose()[\n", + " \"Long business summary\"\n", + "][0]\n", + "overview" + ] + }, + { + "cell_type": "markdown", + "id": "f8a0d747", + "metadata": {}, + "source": [ + "## Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aca4b6ef", + "metadata": {}, + "outputs": [], + "source": [ + "(\n", + " df_year_estimates,\n", + " df_quarter_earnings,\n", + " df_quarter_revenues,\n", + ") = openbb.stocks.dd.models.business_insider.get_estimates(ticker)" + ] + }, + { + "cell_type": "markdown", + "id": "b9c8feff", + "metadata": { + "tags": [] + }, + "source": [ + "### 1. Yearly Estimates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0f0a1ca", + "metadata": {}, + "outputs": [], + "source": [ + "display_year = sorted(df_year_estimates.columns.tolist())[:3]\n", + "df_year_estimates = df_year_estimates[display_year].head(5)\n", + "df_year_estimates" + ] + }, + { + "cell_type": "markdown", + "id": "64d3d490", + "metadata": { + "tags": [] + }, + "source": [ + "### 2. Quarterly Earnings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fde46353", + "metadata": {}, + "outputs": [], + "source": [ + "df_quarter_earnings" + ] + }, + { + "cell_type": "markdown", + "id": "d13a1e5e", + "metadata": { + "tags": [] + }, + "source": [ + "### 3. Quarterly Revenues" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51dfd9d2", + "metadata": {}, + "outputs": [], + "source": [ + "df_quarter_revenues" + ] + }, + { + "cell_type": "markdown", + "id": "6303e520", + "metadata": { + "tags": [] + }, + "source": [ + "### 4. SEC Filings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3022130e", + "metadata": {}, + "outputs": [], + "source": [ + "df_sec_filings = openbb.stocks.dd.models.marketwatch.get_sec_filings(ticker)[\n", + " [\"Type\", \"Category\", \"Link\"]\n", + "].head(5)\n", + "df_sec_filings" + ] + }, + { + "cell_type": "markdown", + "id": "1e201003", + "metadata": { + "tags": [] + }, + "source": [ + "### 5. Analyst Ratings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4662b1ac", + "metadata": {}, + "outputs": [], + "source": [ + "df_analyst = openbb.stocks.dd.models.finviz.get_analyst_data(ticker)\n", + "df_analyst[\"target_to\"] = df_analyst[\"target_to\"].combine_first(df_analyst[\"target\"])\n", + "df_analyst = df_analyst[[\"category\", \"analyst\", \"rating\", \"target_to\"]].rename(\n", + " columns={\n", + " \"category\": \"Category\",\n", + " \"analyst\": \"Analyst\",\n", + " \"rating\": \"Rating\",\n", + " \"target_to\": \"Price Target\",\n", + " }\n", + ")\n", + "df_analyst" + ] + }, + { + "cell_type": "markdown", + "id": "ade589d6-d80f-4651-acb7-929943114ef0", + "metadata": {}, + "source": [ + "## Plots" + ] + }, + { + "cell_type": "markdown", + "id": "621ed931-57e9-4ca6-afe8-8ef278122c77", + "metadata": {}, + "source": [ + "### 1. Price history" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "394668e1-1158-4a2b-8a38-04c72a6abdd6", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (candles, volume) = plt.subplots(nrows=2, ncols=1, figsize=(5, 3), dpi=150)\n", + "openbb.stocks.candle(\n", + " s_ticker=ticker,\n", + " df_stock=ticker_data,\n", + " use_matplotlib=True,\n", + " external_axes=[candles, volume],\n", + ")\n", + "candles.set_xticklabels(\"\")\n", + "fig.tight_layout()\n", + "\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "price_chart = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "markdown", + "id": "bf07a909", + "metadata": {}, + "source": [ + "### 2. Price Target" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "900686a3", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 3), dpi=150)\n", + "openbb.stocks.dd.pt(\n", + " ticker=ticker,\n", + " start=\"2021-10-25\",\n", + " interval=\"1440min\",\n", + " stock=ticker_data,\n", + " num=10,\n", + " raw=False,\n", + " external_axes=[ax],\n", + ")\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "price_target_chart = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "markdown", + "id": "63c0538c", + "metadata": {}, + "source": [ + "### 3. Ratings over time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3733ff2", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 3), dpi=150)\n", + "openbb.stocks.dd.rot(\n", + " ticker=ticker,\n", + " num=10,\n", + " raw=False,\n", + " export=\"\",\n", + " external_axes=[ax],\n", + ")\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "ratings_over_time_chart = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "markdown", + "id": "ac501a32-124d-4d21-aae1-a8d147bb1071", + "metadata": {}, + "source": [ + "## Render the report template to a file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "mounted-phase", + "metadata": {}, + "outputs": [], + "source": [ + "body = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abfe37e4-ea38-42c3-8fb5-7cceb233fb12", + "metadata": {}, + "outputs": [], + "source": [ + "# Title\n", + "body += openbb.widgets.h(1, report_title)\n", + "\n", + "body += openbb.widgets.h(2, \"Overview\")\n", + "body += openbb.widgets.row([openbb.widgets.p(overview)])\n", + "\n", + "\n", + "# Analysts ratings\n", + "body += openbb.widgets.h(2, \"Analyst assessments\")\n", + "body += openbb.widgets.row([price_target_chart])\n", + "body += openbb.widgets.row([df_analyst.to_html()])\n", + "body += openbb.widgets.row([ratings_over_time_chart])\n", + "\n", + "# Price history and yearly estimates\n", + "body += openbb.widgets.row(\n", + " [\n", + " openbb.widgets.h(3, \"Price history\") + price_chart,\n", + " openbb.widgets.h(3, \"Estimates\") + df_year_estimates.head().to_html(),\n", + " ]\n", + ")\n", + "\n", + "# Earnings and revenues\n", + "body += openbb.widgets.h(2, \"Earnings and revenues\")\n", + "body += openbb.widgets.row([df_quarter_earnings.head().to_html()])\n", + "body += openbb.widgets.row([df_quarter_revenues.head().to_html()])\n", + "\n", + "# Sec filings and insider trading\n", + "body += openbb.widgets.h(2, \"SEC filings\")\n", + "body += openbb.widgets.row([df_sec_filings.to_html()])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb26492f-d13f-4302-b36d-ea8ee0020f82", + "metadata": {}, + "outputs": [], + "source": [ + "report = openbb.widgets.html_report(title=report_name, stylesheet=stylesheet, body=body)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee07c30c-9b38-4035-b128-20b7c9989951", + "metadata": {}, + "outputs": [], + "source": [ + "# to save the results\n", + "with open(report_name + \".html\", \"w\") as fh:\n", + " fh.write(report)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34991096-bc3a-4134-b519-aa3a490abe98", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "interpreter": { + "hash": "31090e8428bc3cbe850895ffb75aca3a30f914a21038b56ab61f04d2ddd7301b" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/jupyter/reports/econ_data.ipynb b/openbb_terminal/jupyter/reports/econ_data.ipynb new file mode 100644 index 00000000000..c4e9799de42 --- /dev/null +++ b/openbb_terminal/jupyter/reports/econ_data.ipynb @@ -0,0 +1,648 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "another-behalf", + "metadata": {}, + "outputs": [], + "source": [ + "# Import most generic modules\n", + "import importlib\n", + "import pathlib\n", + "import os\n", + "import sys\n", + "from datetime import datetime\n", + "from IPython.display import display, Markdown\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "module_path = os.path.abspath(os.path.join(\"../..\"))\n", + "\n", + "if module_path not in sys.path:\n", + " sys.path.append(module_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "racial-graduation", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "report_name = f\"{datetime.now().strftime('%Y%m%d_%H%M%S')}_econ_data\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "commercial-cargo", + "metadata": {}, + "outputs": [], + "source": [ + "display(Markdown(f\"# Economy Data - {datetime.now().strftime('%Y/%m/%d %H:%M:%S')}\"))" + ] + }, + { + "cell_type": "markdown", + "id": "informative-behavior", + "metadata": {}, + "source": [ + "## Dow Jones - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "stylish-elite", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"^DJI\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=720,\n", + " low_trend=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "fifty-happiness", + "metadata": {}, + "source": [ + "## Dow Jones - 1 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "identical-classification", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"^DJI\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=360,\n", + " low_trend=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "boxed-turning", + "metadata": {}, + "source": [ + "## S&P 500 - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "threaded-language", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"^GSPC\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=720,\n", + " low_trend=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "fleet-transparency", + "metadata": {}, + "source": [ + "## Market Volatility (VIX) - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "disturbed-workstation", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"^VIX\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=False,\n", + " time_delta=720,\n", + " high_trend=False,\n", + " low_trend=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "civilian-november", + "metadata": {}, + "source": [ + "## Market Volatility (VIX) - 1 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "several-inventory", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"^VIX\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=False,\n", + " time_delta=360,\n", + " high_trend=False,\n", + " low_trend=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bizarre-record", + "metadata": {}, + "source": [ + "## Yield on 5-Year US Treasury Notes - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "military-temperature", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"^FVX\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=False,\n", + " time_delta=720,\n", + " low_trend=False,\n", + " high_trend=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "greater-party", + "metadata": {}, + "source": [ + "## Yield on 10-Year US Treasury Notes - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aboriginal-violin", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"^TNX\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=False,\n", + " time_delta=720,\n", + " low_trend=False,\n", + " high_trend=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "continent-sally", + "metadata": {}, + "source": [ + "## Yield on 30-Year US Treasury Notes - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "formed-heating", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"^TYX\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=False,\n", + " time_delta=720,\n", + " low_trend=False,\n", + " high_trend=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "handled-insurance", + "metadata": {}, + "source": [ + "## Gold - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "advisory-johnston", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"GLD\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=720,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "played-writer", + "metadata": {}, + "source": [ + "## Silver - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "light-harvest", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"SLV\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=720,\n", + " low_trend=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "floral-giant", + "metadata": {}, + "source": [ + "## Euro vs US Dollar - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "corrected-estimate", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"FXE\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=720,\n", + " low_trend=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "neither-montana", + "metadata": {}, + "source": [ + "## Yen vs US Dollar - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "middle-geneva", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"FXY\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=720,\n", + " low_trend=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "theoretical-grammar", + "metadata": {}, + "source": [ + "## Energy - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "swedish-thickness", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"XLE\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=720,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "gentle-humor", + "metadata": {}, + "source": [ + "## Crude Oil - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "instructional-serve", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"CL=F\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=720,\n", + " low_trend=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "classical-listening", + "metadata": {}, + "source": [ + "## Real Estate - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "enclosed-administration", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"XLRE\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=720,\n", + " low_trend=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "turned-percentage", + "metadata": {}, + "source": [ + "## Real Estate - 1 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "sharing-removal", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"VNQ\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=360,\n", + " low_trend=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "swedish-happiness", + "metadata": {}, + "source": [ + "## Financials - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "beginning-aspect", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"XLF\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=720,\n", + " low_trend=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "restricted-glucose", + "metadata": {}, + "source": [ + "## Financials - 1 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "personal-whole", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"XLF\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=360,\n", + " low_trend=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "detailed-strike", + "metadata": {}, + "source": [ + "## Materials - 2 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "negative-mitchell", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"XLB\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=720,\n", + " low_trend=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "boolean-rotation", + "metadata": {}, + "source": [ + "## Materials - 1 year trend with 20, 50 and 200 day SMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "settled-orange", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.economy import econ_data_helper\n", + "\n", + "econ_data_helper.draw_graph(\n", + " \"XLB\",\n", + " \"\",\n", + " line_type=\"line\",\n", + " draw_mas=(20, 50, 200),\n", + " draw_volume=True,\n", + " time_delta=360,\n", + " low_trend=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "premium-transportation", + "metadata": {}, + "outputs": [], + "source": [ + "!jupyter nbconvert {report_name + \".ipynb\"} --to html --no-input" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "", + "language": "python", + "name": "" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/jupyter/reports/similar_analysis.ipynb b/openbb_terminal/jupyter/reports/similar_analysis.ipynb new file mode 100644 index 00000000000..9c19548df0f --- /dev/null +++ b/openbb_terminal/jupyter/reports/similar_analysis.ipynb @@ -0,0 +1,397 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "phantom-facility", + "metadata": {}, + "outputs": [], + "source": [ + "# Import most generic modules\n", + "import importlib\n", + "import pathlib\n", + "import os\n", + "import sys\n", + "from datetime import datetime, timedelta\n", + "import pandas as pd\n", + "from IPython.display import display, Markdown\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "module_path = os.path.abspath(os.path.join(\"../..\"))\n", + "\n", + "if module_path not in sys.path:\n", + " sys.path.append(module_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "compact-count", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Parameters that will be replaced when calling this notebook\n", + "ticker = \"TSLA\"\n", + "\n", + "report_name = f\"{datetime.now().strftime('%Y%m%d_%H%M%S')}_comparison_{ticker.upper()}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "possible-constraint", + "metadata": {}, + "outputs": [], + "source": [ + "display(\n", + " Markdown(\n", + " f\"# {ticker.upper()} - Comparison - {datetime.now().strftime('%Y/%m/%d %H:%M:%S')}\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9eaca4a-a46d-45a3-9306-e80e74234f12", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.comparison_analysis import (\n", + " finviz_compare_model,\n", + " yahoo_finance_model,\n", + ")\n", + "\n", + "ticker = ticker.upper()\n", + "finviz_similar = finviz_compare_model.get_similar_companies(\n", + " ticker, [\"Sector\", \"Industry\", \"Country\"]\n", + ")[0]" + ] + }, + { + "cell_type": "markdown", + "id": "ffea4266-e801-4a4e-8cac-3d60e735aa4e", + "metadata": {}, + "source": [ + "### Similar companies and descriptions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21122245-457b-4347-b89f-d4aabb5bdc9a", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.fundamental_analysis import yahoo_finance_model\n", + "\n", + "df_info = yahoo_finance_model.get_info(ticker)\n", + "if \"Long business summary\" in df_info.index:\n", + " summary = df_info.loc[\"Long business summary\"].values[0]\n", + "if \"Website\" in df_info.index:\n", + " website = df_info.loc[\"Website\"].values[0]\n", + "\n", + "if finviz_similar:\n", + " print(f\"{ticker}: {website}\")\n", + " print(summary)\n", + "\n", + " for symbol in finviz_similar:\n", + " df_info = yahoo_finance_model.get_info(symbol)\n", + " if \"Long business summary\" in df_info.index:\n", + " summary = df_info.loc[\"Long business summary\"].values[0]\n", + " if \"Website\" in df_info.index:\n", + " website = df_info.loc[\"Website\"].values[0]\n", + "\n", + " print(\"\")\n", + " print(f\"{symbol}: {website}\")\n", + " print(summary)" + ] + }, + { + "cell_type": "markdown", + "id": "temporal-clerk", + "metadata": { + "tags": [] + }, + "source": [ + "### Historical prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1fdfac4c-62cd-4bd3-b7df-41ba67263798", + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "from openbb_terminal.stocks.comparison_analysis import yahoo_finance_view\n", + "\n", + "if finviz_similar and finviz_similar != [\"\"]:\n", + " for i in range(math.ceil(len(finviz_similar) / 4)):\n", + " yahoo_finance_view.display_historical(\n", + " similar_tickers=finviz_similar[4 * (i) : 4 * (i + 1)],\n", + " )\n", + "else:\n", + " print(\"Ticker not found in CoinGeckoAPI\")" + ] + }, + { + "cell_type": "markdown", + "id": "2a07f866-7939-4fb7-8b14-c0701087a8ae", + "metadata": {}, + "source": [ + "### Historical correlation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb1850dc-1d62-4ad4-971f-1bbce6209726", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "if finviz_similar and finviz_similar != [\"\"]:\n", + " plt.figure(figsize=(25, 10))\n", + " yahoo_finance_view.display_correlation(\n", + " similar_tickers=finviz_similar,\n", + " )\n", + "else:\n", + " print(\"Ticker not found in CoinGeckoAPI\")" + ] + }, + { + "cell_type": "markdown", + "id": "21005668-7f21-4884-a231-846130695602", + "metadata": { + "tags": [] + }, + "source": [ + "### Historical volumes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7bf528c-427c-4d84-8cb9-ec5656f920e1", + "metadata": {}, + "outputs": [], + "source": [ + "if finviz_similar and finviz_similar != [\"\"]:\n", + " for i in range(math.ceil(len(finviz_similar) / 4)):\n", + " yahoo_finance_view.display_volume(\n", + " similar_tickers=finviz_similar[4 * (i) : 4 * (i + 1)],\n", + " )\n", + "else:\n", + " print(\"Ticker not found in CoinGeckoAPI\")" + ] + }, + { + "cell_type": "markdown", + "id": "6e370470-8014-4e97-95f2-13b4f66645c5", + "metadata": { + "tags": [] + }, + "source": [ + "### Overview" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f82b7162-f7e5-4d5c-8ca9-50466f789f48", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.comparison_analysis import finviz_compare_view\n", + "\n", + "if finviz_similar and finviz_similar != [\"\"]:\n", + " finviz_compare_view.screener(\n", + " similar=finviz_similar,\n", + " data_type=\"overview\",\n", + " )\n", + "else:\n", + " print(\"Ticker not found in CoinGeckoAPI\")" + ] + }, + { + "cell_type": "markdown", + "id": "5277ae24-b7d7-48e6-b46c-fcffdf84631f", + "metadata": { + "tags": [] + }, + "source": [ + "### Valuation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "caed21ad-a31d-4063-a4cb-f5b6fd1c63ed", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.comparison_analysis import finviz_compare_view\n", + "\n", + "if finviz_similar and finviz_similar != [\"\"]:\n", + " finviz_compare_view.screener(\n", + " similar=finviz_similar,\n", + " data_type=\"valuation\",\n", + " )\n", + "else:\n", + " print(\"Ticker not found in CoinGeckoAPI\")" + ] + }, + { + "cell_type": "markdown", + "id": "98030d12-9a77-4f08-b4ad-2c558d9ad225", + "metadata": { + "tags": [] + }, + "source": [ + "### Financial" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e98bf90d-bf2b-4eea-b533-08211e8eec47", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.comparison_analysis import finviz_compare_view\n", + "\n", + "if finviz_similar and finviz_similar != [\"\"]:\n", + " finviz_compare_view.screener(\n", + " similar=finviz_similar,\n", + " data_type=\"financial\",\n", + " )\n", + "else:\n", + " print(\"Ticker not found in CoinGeckoAPI\")" + ] + }, + { + "cell_type": "markdown", + "id": "f7cf8774-5162-47ea-8bfe-5005ed2fb9d9", + "metadata": { + "tags": [] + }, + "source": [ + "### Ownership" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3f90d1f-7a40-403f-bf3e-17703e151f4b", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.comparison_analysis import finviz_compare_view\n", + "\n", + "if finviz_similar and finviz_similar != [\"\"]:\n", + " finviz_compare_view.screener(\n", + " similar=finviz_similar,\n", + " data_type=\"ownership\",\n", + " )\n", + "else:\n", + " print(\"Ticker not found in CoinGeckoAPI\")" + ] + }, + { + "cell_type": "markdown", + "id": "c1833238-5ea8-486b-a6e7-6043e7c0aa0e", + "metadata": { + "tags": [] + }, + "source": [ + "### Performance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62057b47-2364-49ed-80e7-7d6938f2819d", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.comparison_analysis import finviz_compare_view\n", + "\n", + "if finviz_similar and finviz_similar != [\"\"]:\n", + " finviz_compare_view.screener(\n", + " similar=finviz_similar,\n", + " data_type=\"performance\",\n", + " )\n", + "else:\n", + " print(\"Ticker not found in CoinGeckoAPI\")" + ] + }, + { + "cell_type": "markdown", + "id": "6d0494c0-e66a-414a-ba2d-89e4072e1a8f", + "metadata": { + "tags": [] + }, + "source": [ + "### Technical" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1178a080-83ce-4170-a355-9faa8cf9abe6", + "metadata": {}, + "outputs": [], + "source": [ + "from openbb_terminal.stocks.comparison_analysis import finviz_compare_view\n", + "\n", + "if finviz_similar and finviz_similar != [\"\"]:\n", + " finviz_compare_view.screener(\n", + " similar=finviz_similar,\n", + " data_type=\"technical\",\n", + " )\n", + "else:\n", + " print(\"Ticker not found in CoinGeckoAPI\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "sought-phase", + "metadata": {}, + "outputs": [], + "source": [ + "!jupyter nbconvert {report_name + \".ipynb\"} --to html --no-input" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/website/content/api/_index.md b/website/content/api/_index.md index f3c60f94325..3772f781996 100644 --- a/website/content/api/_index.md +++ b/website/content/api/_index.md @@ -20,7 +20,7 @@ scripts and IPython Notebooks (Jupyter). The API wraps the functionality of the terminal in a way that the python commands follow the same convention as the terminal. -For example `stocks/load aapl` becomes `gst.stocks.load("aapl")`. +For example `stocks/load aapl` becomes `openbb.stocks.load("aapl")`. --- @@ -29,7 +29,7 @@ For example `stocks/load aapl` becomes `gst.stocks.load("aapl")`. Import the GST API into your python script or notebook with ```python -from openbb_terminal import api as gst +from openbb_terminal import api as openbb ``` ## Understanding the API functions @@ -39,7 +39,7 @@ docstring. Docstrings can be viewed using the code introspection tools of your text/notebook editor or the built-in python `help` command. Docstrings contain both the description of the functionality and the overview of the parameters. -As an example this is the docstring of `gst.economy.bigmac`: +As an example this is the docstring of `openbb.economy.bigmac`: ```txt Display Big Mac Index for given countries @@ -61,47 +61,47 @@ external_axes : Optional[List[plt.Axes]], optional Load stock data into a dataframe: ```python -aapl_data = gst.stocks.load( +aapl_data = openbb.stocks.load( ticker="aapl", start="2021-06-10", ) -aapl_data = gst.stocks.process_candle(aapl_data) +aapl_data = openbb.stocks.process_candle(aapl_data) ``` Plot candle chart ```python -gst.stocks.candle("AAPL", aapl_data, True) +openbb.stocks.candle("AAPL", aapl_data, True) ``` Call the "What if" command of the backtesting menu ```python -gst.stocks.bt.whatif("aapl", 10, datetime.strptime("2012-03-03", "%Y-%m-%d")) +openbb.stocks.bt.whatif("aapl", 10, datetime.strptime("2012-03-03", "%Y-%m-%d")) ``` Get sentiment for a list of tickers ```python -gst.stocks.ca.models.finbrain.get_sentiments(tickers=["aapl"]) +openbb.stocks.ca.models.finbrain.get_sentiments(tickers=["aapl"]) ``` Print key fundamental metrics for past 2 years ```python -gst.stocks.fa.fmp.metrics(ticker="AAPL", number=2) +openbb.stocks.fa.fmp.metrics(ticker="AAPL", number=2) ``` Get fundamental metrics for past 3 years as a pandas dataframe ```python -gst.stocks.fa.fmp.models.fmp.get_key_metrics(ticker="AAPL", number=3) +openbb.stocks.fa.fmp.models.fmp.get_key_metrics(ticker="AAPL", number=3) ``` Perform an OLS regression on multiple custom datasets ```python -gst.econometrics.ols( +openbb.econometrics.ols( regression_variables=[ 'close-tsla', 'volume-aapl' ], @@ -124,14 +124,14 @@ Example: ```python # Load stock data ticker = "AAPL" -stock_data = gst.stocks.load(ticker="aapl") +stock_data = openbb.stocks.load(ticker="aapl") # Create a matplotlib figure fig, axes = plt.subplots(3, 1, figsize=(10, 7)) # Use GST to add charts to the axes of the created figure -gst.stocks.ta.macd(s_ticker=ticker, series=aapl["Close"], external_axes=axes[0:2]) -gst.stocks.options.voi_yf( +openbb.stocks.ta.macd(s_ticker=ticker, series=aapl["Close"], external_axes=axes[0:2]) +openbb.stocks.options.voi_yf( ticker=ticker, expiry="2022-03-11", min_sp=110, diff --git a/website/content/terminal/portfolio/load/_index.md b/website/content/terminal/portfolio/load/_index.md index 8f073f53981..1e1edc3a1b0 100644 --- a/website/content/terminal/portfolio/load/_index.md +++ b/website/content/terminal/portfolio/load/_index.md @@ -2,7 +2,7 @@ usage: load [-n NAME] [-h] ``` -Load a file from gamestonk_terminal/portfolio/portfolios +Load a file from openbb_terminal/portfolio/portfolios ``` optional arguments: diff --git a/website/content/terminal/portfolio/save/_index.md b/website/content/terminal/portfolio/save/_index.md index cbfdce38b11..0326cd409d9 100644 --- a/website/content/terminal/portfolio/save/_index.md +++ b/website/content/terminal/portfolio/save/_index.md @@ -2,7 +2,7 @@ usage: save [-n NAME] [-h] ``` -Save a file from gamestonk_terminal/portfolio/portfolios +Save a file from openbb_terminal/portfolio/portfolios ``` optional arguments: